Biology subjects

Styner, M.

Publications and source records attributed to Styner, M..

14 recordsLinked to original sources

bioRxiv2026

MedMCP: An Open Agentic Ecosystem for Medical Imaging Workflows

Deep learning has driven rapid progress in medical image analysis, with methods for tasks such as organ segmentation, anomaly detection, and radiology report generation now matching or surpassing expert performance on several benchmarks. While many methods are released as open source, their routine adoption by clinicians and medical researchers remains limited. The bottleneck is not a lack of capable tools, but the absence of infrastructure to make them accessible to non-technical users. To break this barrier, we present MedMCP, an agentic framework that solves this last-mile problem along two axes: (1) a local agentic workspace that lets users apply state-of-the-art image analysis methods through natural language, and (2) a community-driven tool ecosystem that lets developers deploy and distribute capabilities as validated, independently testable servers. MedMCP enforces a strict orchestration-execution boundary in which a local language model translates user intent into workflow plans, while all image analysis is delegated to domain-specific tools. The framework runs entirely on-premise, every side-effecting operation passes through a human-in-the-loop permission gate, and a full provenance trail links each output to the tools, parameters, and inputs that produced it. An inspectable workspace lets users verify intermediate results rather than blindly trusting the agent, and completed sessions distill into deterministic, shareable workflows that re-execute without the agent, turning one-off interactions into reproducible analyses. This paper introduces the project's general idea and describes the design principles that guide its development. Beyond a single toolkit, we envision MedMCP as a community-driven platform where each contributed tool becomes easily accessible, compounding individual efforts into shared capability.

bioinformatics↗

bioRxiv2026

Variation in infant subcortical brain development from 6 to 12 months in Down syndrome

IntroductionDown syndrome (DS), arising from Trisomy 21, is the most common genetic condition associated with intellectual disability. While smaller total brain volumes have been consistently observed in DS, no longitudinal neuroimaging studies have examined volumetric brain development in DS during infancy, a period of rapid neural growth when interventions may have the greatest impact. MethodHigh-resolution T1- and T2-weighted images were acquired during natural sleep in a multisite longitudinal cohort of 44 infants with DS and 39 control infants without DS at ages 6 and 12 months. Neuroimaging data were harmonized to reduce batch effects, and a novel deep-learning, repeated-measures segmentation approach was applied to optimize neuroanatomical segmentations. Total intracranial volume (ICV) and bilateral absolute subcortical volumes (amygdala, caudate, hippocampus, pallidum, putamen, thalamus) were first directly compared in infants with and without DS at 6 and 12 months. Hierarchical linear modeling (HLM) evaluated longitudinal group differences for each structure, accounting for sex, gestational age, and laterality. Subcortical group differences estimated by HLM were also compared to group differences in total ICV. ResultsICV in infants with DS was lower than controls at 6 months (12.6%; p<.001) and 12 months (16.3%; p<.001). Subcortical structures displayed a range of lower volumes (6.9%-13.1%; ps[≤].003) in infants with DS, although the caudate and putamen were exceptions. Caudate volumes were on average lower in DS but not significantly different from controls, while putamen volumes were on average higher in DS but not significantly different from controls, except for the right putamen, which was significantly larger (5.3%; p=.018) at 6 months. In HLM, ICV and all subcortical structures showed slower growth in DS from 6 to 12 months, except for the amygdala and putamen, which displayed similar growth rates to controls. DS-associated reductions in subcortical volumes were similar in magnitude to ICV, although 12-month caudate and 6- and 12-month putamen volumes were enlarged relative to ICV. ConclusionInfants with DS exhibited substantially reduced ICV and widespread reductions in subcortical volumes and growth from 6-12 months. Across a range of volumetric differences, findings were most distinct in the basal ganglia, for which volume reductions were attenuated in the caudate, while the putamen was uniquely enlarged with comparable growth to controls. These observations support early regional specificity in the neural impact of Trisomy 21 and underscore the utility of infant neuroimaging to inform biologically based interventions and clinical trial readiness in DS.

neuroscience↗

bioRxiv2026

Increased CSF volume, altered brain development and emotional reactivity after postnatal Zika virus infection in infant rhesus macaques

Although congenital Zika virus (ZIKV) syndrome is well-characterized, the neurodevelopmental consequences of postnatal infection are less understood. Here we used a rhesus macaque model to investigate the developmental consequences of ZIKV infection during infancy on the brain and behavior, building on our prior research. Male and female infant rhesus macaques infected with ZIKV at 1 month of age were compared to sex-, age-, and rearing-matched uninfected controls and infants treated with the TLR3 agonist PolyIC as a control for activation of the innate immune system. Longitudinal behavioral assessments revealed alterations in emotional regulation following ZIKV exposure, including poor state control scores obtained from the Infant Neurobehavioral Assessment Scale early after ZIKV infection and longer-term displays of increased hostility during an acute stressor. While attachment bonds to caregivers were preserved, ZIKV-infected infants showed sex-specific alterations in behavioral regulation during caregiver separation compared to controls. At 3 months of age, MRI scans revealed larger total cerebrospinal fluid (CSF) volume and reduced volumes in visual processing regions in ZIKV-infected infants compared to controls. Postnatal ZIKV exposure also resulted in sex-specific brain structural alterations with males exhibiting amygdala hypertrophy, whereas ZIKV-infected females had volumetric reductions in temporal-limbic and temporal-auditory cortices. These findings demonstrate that postnatal ZIKV infection disrupts the development of sensory, social and emotion-regulatory systems and CSF function, highlighting the critical need for long-term monitoring of exposed children. One-Sentence SummaryPostnatal Zika virus infection disrupts emotional regulation and alters brain development in infant rhesus macaques, revealing a critical window of neurodevelopmental vulnerability that extends beyond the fetal period.

neuroscience↗

bioRxiv2026

Mechanisms of disrupted neurodevelopment after Zika virus infection in infancy

Congenital and early-life Zika virus (ZIKV) infection can result in neurologic deficits. Precise mechanisms of injury, especially in the more subtle presentation of postnatal infection, are not fully elucidated. Here, we defined the effects of ZIKV on the developing brain using single cell transcriptomics, histopathology and design-based stereology, diffusion MRI, and neurobehavioral assessments in infant rhesus macaques. ZIKV upregulated interferon-stimulated genes in activated microglia and cell death pathways in neurons and downregulated metabolism and differentiation genes in mature oligodendrocytes. Abnormal micro-organization of the corpus collosum and limbic white matter tracts was seen on diffusion weighted imaging. A curated gene set associated with autism spectrum disorder risk was negatively enriched in inhibitory and excitatory neurons from ZIKV-infected infants, with increased emotional reactivity already evident two weeks following infection. From single cells to organism-level behaviors, these results define the pathways and processes disrupted by early-life ZIKV infection.

neuroscience↗

bioRxiv2025

Impacts of perinatal factors on white matter outcome at 8 to 10 years by diffusion tensor imaging

BackgroundWhile perinatal factors are known to influence brain development, their long-term impact on white matter microstructure remains incompletely understood. Previous studies using tract-based spatial statistics (TBSS) have shown limited associations between neonatal measures and later white matter development. MethodsWe investigated associations between perinatal factors (birth weight [BW], gestational age [GA], and head circumference [HC]) and white matter microstructure in 117 children aged 8-10 years from the UNC Early Brain Development Study cohort. Diffusion tensor imaging (DTI) data were analyzed using a fiber tract-based framework examining 54 major white matter tracts. Statistical analysis was performed using a functional analysis of fiber tract profiles. ResultsGA and BW showed widespread significant associations with white matter microstructure (38 and 36 out of 54 tracts, respectively), while HC showed limited associations (3 out of 54 tracts). Post-hoc univariate analysis revealed stronger associations with axial diffusivity (AD) compared to radial diffusivity (RD) or fractional anisotropy (FA). AD associations with BW, GA, and HC were found in 30, 31, and 8 tracts, respectively. ConclusionsUsing a fiber tract-based analysis approach, we demonstrated that GA and BW are strongly predictive of white matter organization at school age, while HC showed limited predictive power. The predominant associations with AD suggest these perinatal factors primarily influence axonal organization rather than myelination. These findings enhance our understanding of how early life factors impact long-term brain development.

neuroscience↗

bioRxiv2025

Automated Locus Coeruleus segmentation and age-related signal changes across Neuromelanin-sensitive MRI and T1/T2-weighted images

The locus coeruleus (LC) plays a key role in stress response, attention, and cognitive function, with its integrity closely linked to neuromelanin accumulation. Neuromelanin-sensitive MRI (NM-MRI) has enabled in vivo imaging of the LC, but automated segmentation remains challenging due to its small size, low contrast, and anatomical variability. This study aimed to evaluate the accuracy of automatic LC segmentation using multi-atlas nonlinear warping applied to high-resolution T1/T2-weighted images and to compare LC intensity characteristics across NM-MRI, T1w, and T2w images. Additionally, we examined developmental changes in LC signal from childhood through adulthood. We analyzed MRI data from 134 participants (ages 8[~]64) with NM-MRI, T1w, and T2w scans, alongside 652 participants (ages 6[~]22) from the Human Connectome Project in Development (HCP-D). LC intensity measures were compared across imaging modalities and analyzed for age-related trends. Automatic LC segmentation achieved high spatial overlap with manual tracings (up to 95% after one voxel 2D-dilation). Statistically estimated high (or low)-intensity values (mean {+/-} 2{sigma}) from automated LC masks provided a more stable and noise-resilient alternative to local peak intensity measures. Age-related LC signal changes are most clearly observed in NM-MRI and T2w imaging, with significant developmental shifts emerging during adolescence. These findings support the conventional T2w imaging as an indirect marker of neuromelanin accumulation and highlight the possibility of age-specific analyses in LC imaging studies.

neuroscience↗

bioRxiv2025

Early cell cycle genes in cortical organoid progenitors predict interindividual variability in infant brain growth trajectories

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individuals genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

neuroscience↗

bioRxiv2024

Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

neuroscience↗

bioRxiv2023

BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans

ObjectivesBrain segmentation of infant magnetic resonance (MR) images is vitally important for studying typical and atypical brain development. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model for robust and generalizable brain segmentation leveraging data augmentation and a large sample size of manually annotated images. Experimental DesignIncluded in model training and testing were MR brain images from 90 participants with an age range of 0-8 months (median age 4.6 months). Using the BOBs repository of manually annotated real images along with synthetic segmentation images produced using SynthSeg, the model was trained using a 10-fold procedure. Model performance of segmentations was assessed by comparing BIBSNet, joint label fusion (JLF) inferred segmentation to ground truth segmentations using Dice Similarity Coefficient (DSC). Additionally, MR data along with the FreeSurfer compatible segmentations were processed with the DCAN labs infant-ABCD-BIDS processing pipeline from ground truth, JLF, and BIBSNet to further assess model performance on derivative data, including cortical thickness, resting state connectivity and brain region volumes. Principal ObservationsBIBSNet segmentations outperforms JLF across all regions based on DSC comparisons. Additionally, with processed derived metrics, BIBSNet segmentations outperforms JLF segmentations across nearly all metrics. ConclusionsBIBSNet segmentation shows marked improvement over JLF across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF, produces FreeSurfer-compatible segmentation labels, and can be easily included in other processing pipelines. BIBSNet provides a viable alternative for segmenting the brain in the earliest stages of development.

neuroscience↗

bioRxiv2022

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

genetics↗

bioRxiv2021

Mechanically induced nuclear shuttling of β-catenin requires co-transfer of actin

Mesenchymal stem cells (MSC) respond to environmental forces with both cytoskeletal re-structuring and activation of protein chaperones of mechanical information, {beta}-catenin and Yes-Associated Protein 1 (YAP1). To function, MSCs must differentiate between dynamic forces such as cyclic strains of extracellular matrix due to physical activity and static strains due to ECM stiffening. To delineate how MSCs recognize and respond differently to both force types, we compared effects of dynamic (200 cycles x 2%) and static (1 x 2% hold) strain on nuclear translocation of {beta}-catenin and YAP1 at 3h after force application. Dynamic strain induced nuclear accumulation of {beta}-catenin, and increased cytoskeletal actin structure and cell stiffness, but had no effect on nuclear YAP1 levels. Critically, both nuclear actin and nuclear stiffness increased along with dynamic strain-induced {beta}-catenin transport. Augmentation of cytoskeletal structure using either static strain or lysophosphatidic acid (LPA) did not increase nuclear content of {beta}-catenin or actin, but induced robust nuclear increase in YAP1. As actin binds {beta}-catenin, we considered whether {beta}-catenin, which lacks a nuclear localization signal, was dependent on actin to gain entry to the nucleus. Knockdown of cofilin-1 (Cfl1) or importin-9 (Ipo9), which co-mediate nuclear transfer of G-actin, prevented dynamic strain-mediated nuclear transfer of both {beta}-catenin and actin. In sum, dynamic strain induction of actin re-structuring promotes nuclear transport of G-actin, concurrently supporting nuclear access of {beta}-catenin via mechanisms utilized for actin transport. Thus, dynamic and static strain activate alternative mechanoresponses reflected by differences in the cellular distributions of actin, {beta}-catenin and YAP1. Significance statementCells integrate both static and dynamic mechanical signals through the actin cytoskeleton which is attached to the nuclear envelope, affecting nuclear transport of {beta}-catenin and YAP1. Dynamic strain induces nuclear translocation of {beta}-catenin, but not YAP1, while static strain causes nuclear translocation of YAP1, but not {beta}-catenin. Importantly, nuclear transport of actin is induced by dynamic but not static force. Furthermore, nuclear import of {beta}-catenin depends on cofilin/importin-9 dependent actin transport mechanisms. Thus the presence of {beta}-catenin and YAP1 in the nucleus represent specific responses to regulatory mechanical signals.

cell biology↗

bioRxiv2020

The contribution of hippocampal subfields to the progression of neurodegeneration

Mild cognitive impairment (MCI) is often considered the precursor of Alzheimers disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI progression have often focused on the hippocampus, but have mostly overlooked its intricate structure and subdivisions. Here, we utilized deep learning to delineate the contribution of hippocampal subfields to MCI progression using a total sample of 1157 subjects (349 in the training set, 427 in a validation set and 381 in the testing set). We propose a dense convolutional neural network architecture that differentiates stable and progressive MCI based on hippocampal morphometry. The proposed deep learning model predicted MCI progression with an accuracy of 75.85%. A novel implementation of occlusion analysis revealed marked differences in the contribution of hippocampal subfields to the performance of the model, with presubiculum, CA1, subiculum, and molecular layer showing the most central role. Moreover, the analysis reveals that 10.5% of the volume of the hippocampus was redundant in the differentiation between stable and progressive MCI. Our predictive model uncovers pronounced differences in the contribution of hippocampal subfields to the progression of MCI. The results may reflect the sparing of hippocampal structure in individuals with a slower progression of neurodegeneration.

neuroscience↗

bioRxiv2019

Long-term alterations in brain and behavior after postnatal Zika virus infections in infant macaques

Considering the impact that Zika virus (ZIKV) infection has on the fetal nervous system and given that the postnatal period is also a time of rapid brain growth, it is important to understand the potential neurobehavioral consequences of ZIKV infection during infancy. Postnatal ZIKV infection in a rhesus macaque (RM) model resulted in long-term behavioral, motor, and cognitive changes, including increased emotional reactivity, decreased social contact, loss of balance, and deficits in visual recognition memory at one year of age. Structural and functional MRI showed that ZIKV-infected infant RMs had persistent enlargement of lateral ventricles, smaller volumes and altered functional connectivity between brain areas important for socioemotional behavior, cognitive, and motor function (e.g. amygdala, hippocampus, cerebellum). Neuropathological changes corresponded with neuroimaging results and were consistent with the behavioral and memory deficits. Overall, this study demonstrates that postnatal ZIKV infection of infants in this model has long lasting neurodevelopmental consequences.

neuroscience↗

bioRxiv2018

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-the-art algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, ...), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores.

neuroscience↗